library(DT)
datatable(dt_innova, rownames = FALSE, filter = "top" )

Innova

Raw data measured by Innova are plotted using the codes below:

p <- dt_innova[, time_ := hms::as_hms(paste( hour(datetime), minute(datetime), second(datetime), sep = ":") )]%>% 
  .[, innova_ch := paste("Ch.", innova_ch)] %>%
  setorder(datetime) %>%
  ggplot(aes(time_, sf6_conc, color = innova_ch) ) +
  geom_line() + 
  facet_grid(fct_inorder(format(date(datetime), format = "%b %d"))~., scales = "free_y") +
  scale_color_discrete("Innova channels")+
  # guides(color = guide_legend(nrow = 1)) +
  scale_y_log10("SF6 concentration (ppm)") +
  coord_cartesian(xlim = c(hms::as_hms("10:00:00"),hms::as_hms("22:00:00")) ) #+
  # theme(legend.position = "top")

ggplotly(p)

VelociCalc

Velocity

p <- 
copy(dt_velocicalc)[, time_ := hms::as_hms(paste( hour(datetime), minute(datetime), second(datetime), sep = ":") )]%>% 
  .[, vc_unit := paste("VC-", vc_unit)] %>%
  setorder(datetime) %>%
  ggplot(aes(time_, vel, color = paste("VC-", vc_unit))) +
  geom_line() + 
  facet_grid(fct_inorder(format(date(datetime), format = "%b %d"))~., scales = "free_y") +
  scale_color_discrete("VC unit")+
  coord_cartesian(xlim = c(hms::as_hms("10:00:00"),hms::as_hms("22:00:00")) ) +
  theme(legend.position = "top")

ggplotly(p)

Temperature

p <- 
copy(dt_velocicalc)[, time_ := hms::as_hms(paste( hour(datetime), minute(datetime), second(datetime), sep = ":") )]%>% 
  .[, vc_unit := paste("VC-", vc_unit)] %>%
  setorder(datetime) %>%
  ggplot(aes(time_, temperature, color = vc_unit)) +
  geom_line() + 
  facet_grid(fct_inorder(format(date(datetime), format = "%b %d"))~., scales = "free_y") +
  scale_color_discrete("VC unit")+
  coord_cartesian(xlim = c(hms::as_hms("10:00:00"),hms::as_hms("22:00:00")) ) +
  theme(legend.position = "top")

ggplotly(p)

Relative Humidity

p <- 
copy(dt_velocicalc)[, time_ := hms::as_hms(paste( hour(datetime), minute(datetime), second(datetime), sep = ":") )]%>% 
  .[, vc_unit := paste("VC-", vc_unit)] %>%
  setorder(datetime) %>%
  ggplot(aes(time_, RH, color = vc_unit)) +
  geom_line() + 
  facet_grid(fct_inorder(format(date(datetime), format = "%b %d"))~., scales = "free_y") +
  scale_color_discrete("VC unit")+
  coord_cartesian(xlim = c(hms::as_hms("10:00:00"),hms::as_hms("22:00:00")) ) +
  theme(legend.position = "top")

ggplotly(p)

Wind sensor

Data from $WIMDA <19>

p <- 
  copy(dt_wind_sensor)[, time_ := hms::as_hms(format(datetime, format = "%H:%M:%S") )] %>% 
  .[, wind_sensor_unit := paste("Station", wind_sensor_unit)] %>%
  .[!is.na(time_)] %>%
  setorder(datetime) %>%
  ggplot(aes(time_, vel_mag_WIMDA_19 , color = wind_sensor_unit)) +
  geom_line() + 
  facet_grid(fct_inorder(format(date(datetime), format = "%b %d"))~., scales = "free_y") +
  scale_color_discrete("Wind sensor unit") +
  coord_cartesian(xlim = c(hms::as_hms("10:00:00"),hms::as_hms("22:00:00")) ) +
  theme(legend.position = "top")

ggplotly(p)

Data from $WIMWV <3>

p <- 
  copy(dt_wind_sensor)[, time_ := hms::as_hms(format(datetime, format = "%H:%M:%S") )] %>% 
  .[, wind_sensor_unit := paste("Station", wind_sensor_unit)] %>%
  .[!is.na(time_)] %>%
  setorder(datetime) %>%
  ggplot(aes(time_, vel_mag_WIMWV_3 , color = wind_sensor_unit)) +
  geom_line() + 
  facet_grid(fct_inorder(format(date(datetime), format = "%b %d"))~., scales = "free_y") +
  scale_color_discrete("Wind sensor unit") +
  coord_cartesian(xlim = c(hms::as_hms("10:00:00"),hms::as_hms("22:00:00")) ) +
  theme(legend.position = "top")

ggplotly(p)

Data from $WIMDA <13>

p <- 
  copy(dt_wind_sensor)[, time_ := hms::as_hms(format(datetime, format = "%H:%M:%S") )] %>% 
  .[, wind_sensor_unit := paste("Station", wind_sensor_unit)] %>%
  .[!is.na(time_)] %>%
  setorder(datetime) %>%
  ggplot(aes(time_, vel_angle_WIMDA_13 , color = wind_sensor_unit)) +
  geom_point(size = 0.5) + 
  facet_grid(fct_inorder(format(date(datetime), format = "%b %d"))~., scales = "free_y") +
  scale_color_discrete("Wind sensor unit") +
  coord_cartesian(xlim = c(hms::as_hms("10:00:00"),hms::as_hms("22:00:00")) ) +
  theme(legend.position = "top")

ggplotly(p)

Data from $WIMWV <1>

p <- 
  copy(dt_wind_sensor)[, time_ := hms::as_hms(format(datetime, format = "%H:%M:%S") )] %>% 
  .[, wind_sensor_unit := paste("Station", wind_sensor_unit)] %>%
  .[!is.na(time_)] %>%
  setorder(datetime) %>%
  # .[, test_id := fct_inorder(paste("Test",test_id)) ] %>% 
  ggplot(aes(time_, vel_angle_WIMWV_1 , color = wind_sensor_unit)) +
  geom_point(size = 0.5) + 
  facet_grid(fct_inorder(format(date(datetime), format = "%b %d"))~., scales = "free_y") +
  scale_color_discrete("Wind sensor unit") +
  coord_cartesian(xlim = c(hms::as_hms("10:00:00"),hms::as_hms("22:00:00")) ) +
  theme(legend.position = "top")

ggplotly(p)